• Desarrollo de Personas Sintéticas de IA: Diseñar y desarrollar personas impulsadas por inteligencia artificial utilizando grafos de conocimiento, RAG, GraphRAG y datos empresariales. • Desarrollo de soluciones basadas en LLMs: Implementar funcionalidades avanzadas utilizando modelos de lenguaje para generar insights y apoyar la toma de decisiones. • Diseño de flujos de IA agéntica: Crear y optimizar agentes inteligentes capaces de automatizar tareas complejas y generar información de valor para el negocio. • Desarrollo Backend y Fullstack: Construir APIs, microservicios e integraciones escalables utilizando tecnologías modernas y buenas prácticas de ingeniería de software. • Gestión de datos e integración: Trabajar con plataformas de datos empresariales, conocimiento estructurado y no estructurado para alimentar soluciones de IA. • Optimización y evaluación de soluciones IA: Mejorar continuamente la calidad, precisión, rendimiento y escalabilidad de los componentes desarrollados. • Colaboración multidisciplinar: Trabajar estrechamente con Product Owners, arquitectos, equipos técnicos y stakeholders de negocio dentro de metodologías ágiles.

Role Description DDN is seeking a Client Director – Strategic AI Infrastructure to drive revenue growth in the U.S. West Coast region within our Artificial Intelligence business unit. This is a quota-carrying role responsible for developing strategic enterprise opportunities and expanding DDN’s presence within organizations building large-scale AI and high-performance computing environments. In this role, the Account Executive will operate as a Strategic AI Client Director, partnering with enterprise customers, research institutions, and cloud organizations to deliver high-performance data infrastructure solutions that support advanced AI workloads. The successful candidate will work closely with internal engineering teams, solutions architects, and channel partners to design and deliver complex AI infrastructure solutions supporting GPU computing, large-scale model training, and data-intensive workloads. - Act as a Strategic AI Client Director within the assigned West Coast territory, driving adoption of DDN’s AI infrastructure solutions across enterprise and research organizations. - Drive revenue growth and achieve or exceed assigned sales quota across the West Coast region. - Develop and execute strategic territory and account plans targeting organizations building large-scale AI and high-performance computing environments. - Identify and qualify new opportunities across industries including financial services, healthcare, telecommunications, technology, research institutions, and large enterprise organizations. - Build trusted relationships with senior technical leaders, AI infrastructure teams, and executive decision-makers within target accounts. - Partner with solutions architects and engineering teams to design and present AI infrastructure and high-performance data storage solutions. - Deliver compelling presentations, product demonstrations, and proposals showcasing the value of DDN’s AI-optimized storage platforms. - Manage the full enterprise sales cycle including pipeline development, forecasting, contract negotiation, and deal closure. - Develop strong relationships with channel partners, system integrators, OEM partners, and distributors to expand market reach. - Stay current on market trends related to AI infrastructure, GPU computing, large-scale model training, and high-performance data environments. - Represent DDN at industry events, conferences, and customer engagements to generate new opportunities and strengthen market presence. Qualifications - 8+ years of experience in enterprise technology sales, preferably within storage, compute, networking, or infrastructure platforms. - Demonstrated experience selling AI infrastructure, HPC solutions, cloud platforms, or enterprise data center technologies. - Experience managing complex enterprise sales cycles involving technical buyers and executive stakeholders. Requirements - Experience selling AI infrastructure, GPU clusters, or high-performance computing platforms. - Familiarity with the AI ecosystem including technologies from NVIDIA, Intel, AMD, Cerebras, Graphcore, or Google TPU. - Knowledge of enterprise storage technologies including SAN, NAS, object storage, and parallel file systems. - Experience working with channel partners, system integrators, and distributor ecosystems. - Familiarity with technologies such as Kubernetes, OpenStack, and large-scale AI data center environments. Benefits - At DDN, you will work at the forefront of AI infrastructure innovation, helping organizations solve some of the most demanding data challenges in the world. - Our teams collaborate across engineering, sales, and customer success to deliver cutting-edge technology that powers the next generation of AI breakthroughs.

• Build and own core platform capabilities, user-facing experiences and backend services— delivering end-to-end features from requirements to production. • Drive architecture and technical direction for integration-heavy systems, helping the team make durable decisions around scalability, reliability, maintainability, and operational safety. • Own reliability and production-grade behavior, including safe shutdown and edge-case handling, and proactively debug issues across distributed systems. • Build secure, compliant integrations and user experiences, including stronger validation, security hardening, and privacy-minded product changes. • Deliver user-facing UX improvements that reduce friction and increase clarity, ensuring internal and external tools are fast, intuitive, and accessible. • Collaborate cross-functionally and drive projects to completion, partnering with Product, QA, Design, and other engineering teams to deliver roadmap items and high-severity fixes. • Raise engineering standards through code review and technical mentorship, setting patterns for maintainability, observability, and robust integration design.

• Drive the on-site deployment, integration, and scaling of enterprise Generative AI solutions. • Embed directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP). • Bridge the gap between AI research and production-grade cloud infrastructure. • Collaborate with cross-functional teams and business partners to leverage analytical skills and ensure business value.